内部分析技术用于自动和日常访问监控
Daniel Schneditz1, Werner Ribitsch2, David F Keane3
1Otto Loewi Research Center, Division of Physiology, Medical University of Graz, Graz, Austria.
Seminars in dialysis
|June 27, 2023
概括
监测透析机数据可以帮助预测血管接入血栓形成风险. 这种方法避免了透析的中断,并通过早期识别有风险的接入点来改善患者的护理.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 血管接入功能障碍显著影响透析患者,导致诸如减少透析输送和接入丧失等并发症.
- 评估血管接入性能的传统方法耗时,并且会干扰透析疗程.
- 使用已确定的参考方法预测接入血栓形成风险产生了令人失望的结果.
研究的目的:
- 审查在透析期间持续或间歇监测血管接入功能的新技术.
- 突出综合透析机数据在早期检测访问问题的潜力.
- 探索机器学习和专家系统在识别有风险的血管通道方面的应用.
主要方法:
- 在透析机器中集成的技术的叙述性审查.
- 专注于在透析过程中例行收集的数据,例如体外血流和动态线压.
- 讨论使用专家系统和机器学习分析集成数据.
主要成果:
- 使用集成透析机数据的持续监测提供了一种非破坏性的方法来评估血管接入功能.
- 像血流,线压和循环回流等参数,常规测量,可以表明接入健康.
- 对这些数据的机器学习分析显示,在预测血栓形成风险方面具有前景.
结论:
- 综合透析机数据分析为主动血管接入管理提供了一个有前途的战略.
- 这种方法可以改善对血栓形成风险的入口的识别,而不会影响透析治疗.
- 使用例行收集的数据可以提高患者的治疗结果,并减少医疗保健利用率.
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